This notebook shows line intensity variation along the SUMER slit. Links to Figure B1.

(The internal hyperlink only works on nbviewer. Do not click when viewing the notebook on GitHub.)

In [ ]:
import matplotlib.pyplot as plt
from matplotlib import ticker
import numpy as np
import scipy.io 
import sys
sys.path.append('/Users/yjzhu/Desktop/Solar/MyPy')
import juanfit
import importlib
importlib.reload(juanfit)
from juanfit import SpectrumFitSingle, SpectrumFitRow
import cmcrameri.cm as cmcm
import sumerpy
importlib.reload(sumerpy)
import corner
import mycorner
importlib.reload(mycorner)
from sumerpy import con_width_funct_4, radiometry
from matplotlib.colors import LinearSegmentedColormap
import cmcrameri.cm as cmcm
In [ ]:
sumer_img = scipy.io.readsav("../../save/sumer_merg.sav")["int_merg"]
wvl_338 = np.linspace(0,1023,1024)*0.04496629878144223 + 671.7967194748996
sumer_338_window = sumer_img[:,:1024]
wvl_375 = np.linspace(0,1023,1024)*0.044790246422816475 + 745.655738274089
sumer_375_window = sumer_img[:,1024:2048]
wvl_509 = np.linspace(0,1023,1024)*0.04381162280734919 + 1015.1032588901901
sumer_509_window = sumer_img[:,2048:3072]
wvl_607 = np.linspace(0,1023,1024)*0.04316670824233466 + 1209.3346601414057
sumer_607_window = sumer_img[:,3072:]
In [ ]:
fig, axes = plt.subplots(4,1,figsize=(16,16),constrained_layout=True)

axes[0].pcolormesh(np.arange(1024),np.arange(299,-1,-1),np.nan_to_num(np.log10(sumer_338_window),nan=-np.inf),
                  cmap=cmcm.lajolla_r,shading="auto",vmin=-5)
axes[1].pcolormesh(np.arange(1024),np.arange(299,-1,-1),np.nan_to_num(np.log10(sumer_375_window),nan=-np.inf),
                  cmap=cmcm.lajolla_r,shading="auto",vmin=-5)
axes[2].pcolormesh(np.arange(1024),np.arange(299,-1,-1),np.nan_to_num(np.log10(sumer_509_window),nan=-np.inf),
                  cmap=cmcm.lajolla_r,shading="auto",vmin=-5)
axes[3].pcolormesh(np.arange(1024),np.arange(299,-1,-1),np.nan_to_num(np.log10(sumer_607_window),nan=-np.inf),
                  cmap=cmcm.lajolla_r,shading="auto",vmin=-5)


for ax_, wvl_array in zip(axes, (wvl_338,wvl_375,wvl_509,wvl_607)):
    ax_wvl = ax_.twiny()
    ax_wvl.set_xlim(wvl_array[0],wvl_array[-1])
    ax_.xaxis.set_minor_locator(ticker.AutoMinorLocator(10))
    ax_wvl.xaxis.set_minor_locator(ticker.AutoMinorLocator(10))
    ax_.tick_params(labelsize=16)
    ax_wvl.tick_params(labelsize=16)
    ax_.tick_params(which="major",width=1.2,length=8)
    ax_wvl.tick_params(which="major",width=1.2,length=8)
    ax_.tick_params(which="minor",width=1.2,length=4)
    ax_wvl.tick_params(which="minor",width=1.2,length=8)

/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log10
  This is separate from the ipykernel package so we can avoid doing imports until
/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log10
  """
/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log10
  import sys
/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: RuntimeWarning: divide by zero encountered in log10
  if __name__ == '__main__':
/anaconda3/lib/python3.7/site-packages/matplotlib/colors.py:621: RuntimeWarning: overflow encountered in multiply
  xa *= self.N
In [ ]:
OIII_703_slice = slice(670,700)
wvl_OIII_703 = wvl_338[OIII_703_slice]
radiometry_OIII_703 = radiometry(1,wvl_OIII_703,1,np.ones_like(wvl_OIII_703),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_OIII_703 = np.average(sumer_338_window[100:,OIII_703_slice].reshape(-1,10,OIII_703_slice.stop - OIII_703_slice.start),axis=1)*radiometry_OIII_703
err_OIII_703 = np.sqrt(np.sum(300*sumer_338_window[100:,OIII_703_slice].reshape(-1,10,OIII_703_slice.stop - OIII_703_slice.start),
                        axis=1))/30/300*radiometry_OIII_703

OIII_703_fit_model = SpectrumFitRow(data=int_OIII_703,wvl=wvl_OIII_703,err=err_OIII_703, line_number=2,
                    line_wvl_init=[702.3,702.85],int_max_init=[0.1,0.2],fwhm_init=[0.05,0.05],same_width=False)
OIII_703_fit_model.run_lse()

/Users/yjzhu/Desktop/Solar/MyPy/sumerpy.py:457: UserWarning: No test phase set by default.
  warn("No test phase set by default.")
/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:756: RuntimeWarning: divide by zero encountered in true_divide
  transform = 1.0 / sigma
/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:834: OptimizeWarning: Covariance of the parameters could not be estimated
  category=OptimizeWarning)
In [ ]:
OIII_703_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
OIII_703_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
OV_760_slice = slice(315,345)
wvl_OV_760 = wvl_375[OV_760_slice]
radiometry_OV_760 = radiometry(1,wvl_OV_760,1,np.ones_like(wvl_OV_760),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_OV_760 = np.average(sumer_375_window[100:,OV_760_slice].reshape(-1,10,OV_760_slice.stop - OV_760_slice.start),axis=1)*radiometry_OV_760
err_OV_760 = np.sqrt(np.sum(300*sumer_375_window[100:,OV_760_slice].reshape(-1,10,OV_760_slice.stop - OV_760_slice.start),
                        axis=1))/30/300*radiometry_OV_760

OV_760_fit_model = SpectrumFitRow(data=int_OV_760,wvl=wvl_OV_760,err=err_OV_760, line_number=1,
                    line_wvl_init=[760.4],int_max_init=[0.1],fwhm_init=[0.2],same_width=True,mask=[[759.97,760.25]])
OV_760_fit_model.run_lse()
In [ ]:
OV_760_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
OV_760_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
NIV_765_slice = slice(425,450)
wvl_NIV_765 = wvl_375[NIV_765_slice]
radiometry_NIV_765 = radiometry(1,wvl_NIV_765,1,np.ones_like(wvl_NIV_765),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_NIV_765 = np.average(sumer_375_window[100:,NIV_765_slice].reshape(-1,10,NIV_765_slice.stop - NIV_765_slice.start),axis=1)*radiometry_NIV_765
err_NIV_765 = np.sqrt(np.sum(300*sumer_375_window[100:,NIV_765_slice].reshape(-1,10,NIV_765_slice.stop - NIV_765_slice.start),
                        axis=1))/30/300*radiometry_NIV_765

NIV_765_fit_model = SpectrumFitRow(data=int_NIV_765,wvl=wvl_NIV_765,err=err_NIV_765, line_number=1,
                    line_wvl_init=[765.15],int_max_init=[0.7],fwhm_init=[0.2],same_width=True)
NIV_765_fit_model.run_lse()
In [ ]:
NIV_765_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
NIV_765_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
SV_786_slice = slice(900,925)
wvl_SV_786 = wvl_375[SV_786_slice]
radiometry_SV_786 = radiometry(1,wvl_SV_786,1,np.ones_like(wvl_SV_786),bare=True,kbr=False,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_SV_786 = np.average(sumer_375_window[100:,SV_786_slice].reshape(-1,10,SV_786_slice.stop - SV_786_slice.start),axis=1)*radiometry_SV_786
err_SV_786 = np.sqrt(np.sum(300*sumer_375_window[100:,SV_786_slice].reshape(-1,10,SV_786_slice.stop - SV_786_slice.start),
                        axis=1))/30/300*radiometry_SV_786

SV_786_fit_model = SpectrumFitRow(data=int_SV_786,wvl=wvl_SV_786,err=err_SV_786, line_number=1,
                    line_wvl_init=[786.5],int_max_init=[0.4],fwhm_init=[0.2],same_width=True)
SV_786_fit_model.run_lse()
In [ ]:
SV_786_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
SV_786_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
OIV_787_slice = slice(925,960)
wvl_OIV_787 = wvl_375[OIV_787_slice]
radiometry_OIV_787 = radiometry(1,wvl_OIV_787,1,np.ones_like(wvl_OIV_787),bare=True,kbr=False,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_OIV_787 = np.average(sumer_375_window[100:,OIV_787_slice].reshape(-1,10,OIV_787_slice.stop - OIV_787_slice.start),axis=1)*radiometry_OIV_787
err_OIV_787 = np.sqrt(np.sum(300*sumer_375_window[100:,OIV_787_slice].reshape(-1,10,OIV_787_slice.stop - OIV_787_slice.start),
                        axis=1))/30/300*radiometry_OIV_787

OIV_787_fit_model = SpectrumFitRow(data=int_OIV_787,wvl=wvl_OIV_787,err=err_OIV_787, line_number=1,
                    line_wvl_init=[787.7],int_max_init=[0.4],fwhm_init=[0.2],same_width=True,mask=[[787.25,787.45]])
OIV_787_fit_model.run_lse()
In [ ]:
OIV_787_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
OIV_787_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
Lybeta_slice = slice(200,270)
wvl_Lybeta = wvl_509[Lybeta_slice]
radiometry_Lybeta = radiometry(1,wvl_Lybeta,1,np.ones_like(wvl_Lybeta),bare=True,kbr=False,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_Lybeta = np.average(sumer_509_window[100:,Lybeta_slice].reshape(-1,10,Lybeta_slice.stop - Lybeta_slice.start),axis=1)*radiometry_Lybeta
err_Lybeta = np.sqrt(np.sum(300*sumer_509_window[100:,Lybeta_slice].reshape(-1,10,Lybeta_slice.stop - Lybeta_slice.start),
                        axis=1))/30/300*radiometry_Lybeta

Lybeta_fit_model = SpectrumFitRow(data=int_Lybeta,wvl=wvl_Lybeta,err=err_Lybeta, line_number=1,
                    line_wvl_init=[1025.7],int_max_init=[1],fwhm_init=[1],same_width=True,mask=[[1026.2,1027]])
Lybeta_fit_model.run_lse()
In [ ]:
Lybeta_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
Lybeta_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
OI_1027_slice = slice(270,290)
wvl_OI_1027 = wvl_509[OI_1027_slice]
radiometry_OI_1027 = radiometry(1,wvl_OI_1027,1,np.ones_like(wvl_OI_1027),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_OI_1027 = np.average(sumer_509_window[100:,OI_1027_slice].reshape(-1,10,OI_1027_slice.stop - OI_1027_slice.start),axis=1)*radiometry_OI_1027
err_OI_1027 = np.sqrt(np.sum(300*sumer_509_window[100:,OI_1027_slice].reshape(-1,10,OI_1027_slice.stop - OI_1027_slice.start),
                        axis=1))/30/300*radiometry_OI_1027

OI_1027_fit_model = SpectrumFitRow(data=int_OI_1027,wvl=wvl_OI_1027,err=err_OI_1027, line_number=1,
                    line_wvl_init=[1027.42],int_max_init=[0.2],fwhm_init=[0.2],same_width=True,mask=[[1027.6,1027.77]])
OI_1027_fit_model.run_lse()
In [ ]:
OI_1027_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
OI_1027_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
CII_1036_slice = slice(470,490)
wvl_CII_1036 = wvl_509[CII_1036_slice]
radiometry_CII_1036 = radiometry(1,wvl_CII_1036,1,np.ones_like(wvl_CII_1036),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_CII_1036 = np.average(sumer_509_window[100:,CII_1036_slice].reshape(-1,10,CII_1036_slice.stop - CII_1036_slice.start),axis=1)*radiometry_CII_1036
err_CII_1036 = np.sqrt(np.sum(300*sumer_509_window[100:,CII_1036_slice].reshape(-1,10,CII_1036_slice.stop - CII_1036_slice.start),
                        axis=1))/30/300*radiometry_CII_1036

CII_1036_fit_model = SpectrumFitRow(data=int_CII_1036,wvl=wvl_CII_1036,err=err_CII_1036, line_number=1,
                    line_wvl_init=[1036.3],int_max_init=[0.4],fwhm_init=[0.2],same_width=True)
CII_1036_fit_model.run_lse()
In [ ]:
CII_1036_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
CII_1036_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
Lyalpha_slice = slice(100,200)
wvl_Lyalpha = wvl_607[Lyalpha_slice]
radiometry_Lyalpha = radiometry(1,wvl_Lyalpha,1,np.ones_like(wvl_Lyalpha),bare=True,kbr=False,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_Lyalpha = np.average(sumer_607_window[100:,Lyalpha_slice].reshape(-1,10,Lyalpha_slice.stop - Lyalpha_slice.start),axis=1)*radiometry_Lyalpha
err_Lyalpha = np.sqrt(np.sum(300*sumer_607_window[100:,Lyalpha_slice].reshape(-1,10,Lyalpha_slice.stop - Lyalpha_slice.start),
                        axis=1))/30/300*radiometry_Lyalpha

Lyalpha_fit_model = SpectrumFitRow(data=int_Lyalpha,wvl=wvl_Lyalpha,err=err_Lyalpha, line_number=1,
                    line_wvl_init=[1216],int_max_init=[2],fwhm_init=[1],same_width=True)
Lyalpha_fit_model.run_lse()
In [ ]:
Lyalpha_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
Lyalpha_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
fig, ax = plt.subplots(figsize=(8,6),constrained_layout=True)
ax.plot(np.arange(195,-1,-10), np.sum((wvl_607[1] - wvl_607[0])*sumer_607_window[100:,Lyalpha_slice].reshape(-1,10,Lyalpha_slice.stop - Lyalpha_slice.start),
                        axis=(1,2)))
Out[ ]:
[<matplotlib.lines.Line2D at 0x193c95790>]
In [ ]:
NV_1238_slice = slice(660,710)
wvl_NV_1238 = wvl_607[NV_1238_slice]
radiometry_NV_1238 = radiometry(1,wvl_NV_1238,1,np.ones_like(wvl_NV_1238),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_NV_1238 = np.average(sumer_607_window[100:,NV_1238_slice].reshape(-1,10,NV_1238_slice.stop - NV_1238_slice.start),axis=1)*radiometry_NV_1238
err_NV_1238 = np.sqrt(np.sum(300*sumer_607_window[100:,NV_1238_slice].reshape(-1,10,NV_1238_slice.stop - NV_1238_slice.start),
                        axis=1))/30/300*radiometry_NV_1238

NV_1238_fit_model = SpectrumFitRow(data=int_NV_1238,wvl=wvl_NV_1238,err=err_NV_1238, line_number=1,
                    line_wvl_init=[1238.8],int_max_init=[0.7],fwhm_init=[0.2],same_width=True)
NV_1238_fit_model.run_lse()
In [ ]:
NV_1238_fit_model.plot_fit()
Out[ ]:
array([[<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:ylabel='Intensity'>, <AxesSubplot:>, <AxesSubplot:>,
        <AxesSubplot:>],
       [<AxesSubplot:xlabel='$\\textrm{Wavelength}$', ylabel='Intensity'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>,
        <AxesSubplot:xlabel='$\\textrm{Wavelength}$'>]], dtype=object)
In [ ]:
NV_1238_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
NeVIII_770_slice = slice(540,570)
wvl_NeVIII_770 = wvl_375[NeVIII_770_slice]
radiometry_NeVIII_770 = radiometry(1,wvl_NeVIII_770,1,np.ones_like(wvl_NeVIII_770),bare=False,kbr=True,photons=False,watts=True,det_a=False,
                                det_b=True,joint=False,separate=True)*1.e3

int_NeVIII_770 = np.average(sumer_375_window[100:,NeVIII_770_slice].reshape(-1,10,NeVIII_770_slice.stop - NeVIII_770_slice.start),axis=1)*radiometry_NeVIII_770
err_NeVIII_770 = np.sqrt(np.sum(300*sumer_375_window[100:,NeVIII_770_slice].reshape(-1,10,NeVIII_770_slice.stop - NeVIII_770_slice.start),
                        axis=1))/30/300*radiometry_NeVIII_770

NeVIII_770_fit_model = SpectrumFitRow(data=int_NeVIII_770,wvl=wvl_NeVIII_770,err=err_NeVIII_770, line_number=1,
                    line_wvl_init=[770.4],int_max_init=[1],fwhm_init=[0.3],same_width=True)
NeVIII_770_fit_model.run_lse()
In [ ]:
NeVIII_770_fit_model.plot_variation(var="int",xdata=np.arange(195,-1,-10))

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
Out[ ]:
<AxesSubplot:ylabel='Total Intensity $I_0$'>
In [ ]:
def normalize_int_err(int,err):
    int_norm = int[4:]/np.nanmean(int[4:5])
    int_err_norm = err[4:]/np.nanmean(int[4:5])

    return int_norm, int_err_norm

Figure B1¶

In [ ]:
fig, ax = plt.subplots(figsize=(9.5,8),constrained_layout=True)

OV_760_norm, OV_760_norm_err = normalize_int_err(OV_760_fit_model.int_total_fit[:,0],
                                OV_760_fit_model.int_total_err[:,0])

NIV_765_norm, NIV_765_norm_err = normalize_int_err(NIV_765_fit_model.int_total_fit[:,0],
                                NIV_765_fit_model.int_total_err[:,0])

SV_786_norm, SV_786_norm_err = normalize_int_err(SV_786_fit_model.int_total_fit[:,0],
                                SV_786_fit_model.int_total_err[:,0])

OIV_787_norm, OIV_787_norm_err = normalize_int_err(OIV_787_fit_model.int_total_fit[:,0],
                                OIV_787_fit_model.int_total_err[:,0])

LyBeta_norm, LyBeta_norm_err = normalize_int_err(Lybeta_fit_model.int_total_fit[:,0],
                                Lybeta_fit_model.int_total_err[:,0])

OI_1027_norm, OI_1027_norm_err = normalize_int_err(OI_1027_fit_model.int_total_fit[:,0],
                                OI_1027_fit_model.int_total_err[:,0])

CII_1036_norm, CII_1036_norm_err = normalize_int_err(CII_1036_fit_model.int_total_fit[:,0],
                                CII_1036_fit_model.int_total_err[:,0])

NV_1238_norm, NV_1238_norm_err = normalize_int_err(NV_1238_fit_model.int_total_fit[:,0],
                                NV_1238_fit_model.int_total_err[:,0])

NeVIII_770_norm, NeVIII_770_norm_err = normalize_int_err(NeVIII_770_fit_model.int_total_fit[:,0],
                                NeVIII_770_fit_model.int_total_err[:,0])

ax.errorbar(np.arange(155,-1,-10), OV_760_norm, OV_760_norm_err,label=r"\textbf{O \textsc{v}}",
                                    color="#77AADD",lw=2,marker="o",markersize=5)
ax.errorbar(np.arange(155,-1,-10), NIV_765_norm, NIV_765_norm_err,label=r"\textbf{N \textsc{iv}}",
                                    color="#EE8866",lw=2,marker="v",markersize=5)
ax.errorbar(np.arange(155,-1,-10), SV_786_norm, SV_786_norm_err,label=r"\textbf{S \textsc{v}}",
                                    color="#EEDD88",lw=2,marker="^",markersize=5)
ax.errorbar(np.arange(155,-1,-10), OIV_787_norm, OIV_787_norm_err,label=r"\textbf{O \textsc{iv}}",
                                    color="#FFAABB",lw=2,marker="<",markersize=5)
ax.errorbar(np.arange(155,-1,-10), LyBeta_norm, LyBeta_norm_err,label=r"\textbf{Ly}$\boldsymbol\beta$",
                                    color="#99DDFF",lw=2,marker=">",markersize=5)
ax.errorbar(np.arange(155,-1,-10), OI_1027_norm, OI_1027_norm_err,label=r"\textbf{O \textsc{i}}",
                                    color="#44BB99",lw=2,marker="s",markersize=5)
ax.errorbar(np.arange(155,-1,-10), CII_1036_norm, CII_1036_norm_err,label=r"\textbf{C \textsc{ii}}",
                                    color="#BBCC33",lw=2,marker="D",markersize=5)
ax.errorbar(np.arange(155,-1,-10), NV_1238_norm, NV_1238_norm_err,label=r"\textbf{N \textsc{v}}",
                                    color="#AAAA00",lw=2,marker="P",markersize=5)
ax.errorbar(np.arange(155,-1,-10), NeVIII_770_norm, NeVIII_770_norm_err,label=r"\textbf{Ne \textsc{viii}}",
                                    color="#DDDDDD",lw=2,marker="H",markersize=5)

ax.axvspan(0,30,color="#F5F5F5",zorder=0)

ax.set_yscale("log")
ax.tick_params(labelsize=18,direction="in",which="both",right=True,top=True)
ax.tick_params(which="major",length=8)
ax.tick_params(which="minor",length=6)
ax.set_xlabel(r"\textbf{SUMER Detector-Y [arcsec]}",fontsize=18)
ax.set_ylabel(r"\textbf{Relative Intensity} $\boldsymbol I/I_0$",fontsize=18)
ax.legend(ncol=5,fontsize=18,frameon=False,bbox_to_anchor=(0.,1.05,1,.102),)
plt.savefig(fname="../../figs/ms/SUMER_stray_height.pdf",format="pdf",bbox_inches="tight")